In recent years, research has attempted to achieve image registration by using deep learning methods. In general, relevant methods can be divided into two categories [1]}: using deep learning networks to estimate the similarity measure of two images to drive iterative optimization, and directly using deep regression networks to predict transformation parameters. Deep learning-based methods first achieved good results in medical image registration. More and more research on remote sensing image registration began to shift its focus to the neural network and deep learning. Although the algorithms based on deep learning are relatively advanced, they often need plenty of annotated samples for model training, in which the model is highly targeted. So the deep learning-based methods cannot be widely applied to all kinds of multi-source data, and are not very mature at present.
